This paper presents the first non-interactive, succinct, and secure representation of a PoW-based blockchain that operates under variable mining difficulty while satisfying both completeness and onlineness properties. Completeness ensures that provers can update an existing NIPoPoW by incorporating a newly mined block, whereas onlineness ensures that miners can extend the chain directly from a NIPoPoW. The time complexity for both the prover (to update a NIPoPoW with a new block) and the verifier is logarithmic in the number of blocks of the underlying PoW blockchain. The communication complexity required for synchronization is polylogarithmic in the length of the blockchain. We prove the correctness of our scheme in the presence of a 1/3-bounded PPT adversary.

5G network slicing allows the coexistence of multiple virtualized networks on the same infrastructure. Leveraging this concept, it becomes possible to create marketplaces where Infrastructure Providers (InPs) lease network resources to different Mobile Virtual Network Operators (MVNOs) while accommodating their specific Quality of Service (QoS) requirements. In addition to the cost criteria, an MVNO choosing between several InP might be interested in evaluating the InPs actual capacity to deliver the expected Service Level Agreement (SLA). Existing 5G literature on trust evaluation is often based on blockchain. While this technology offers transparency and auditability, the inherent consensus mechanism often brings additional costs. In this paper, we propose a distributed reputation system based on fuzzy logic that can provide a robust and dynamic estimation of an InP behavior while respecting the subjective requirements of MVNOs. We evaluate this system in a Radio Access Network (RAN) sharing simulation and we show that it can redirect MVNO to InP that are capable of meeting their needs. We further test different trust decay strategy in order to find one that is able to both quickly react to a network outage and forgive InP once the punctual outage is solved.

Cross-silo federated learning (CS-FL) is a distributed learning setting which allows an identified set of organizations to collaboratively train a single global model. Since CS-FL use cases are often heterogeneous, it may be more appropriate to dynamically provide different models to more homogeneous sub-federations. In addition, such systems can be undermined by contributions of poor quality, making negligent or even malicious participants critical to consider. However, distinguishing such participants in a heterogeneous context is especially difficult. We present RADAR, a novel architecture for CS-FL able to assess the quality of the participants' contributions, regardless of data similarity. RADAR leverages client-side evaluation to directly collect feedbacks from the participants. The same evaluations allow grouping participants according to their perceived similarity and weighting the model aggregation based on their reputation. To evaluate our approach on concrete experiments, we implement a collaborative intrusion detection system (CIDS) scenario and test our architecture in various data-quality settings using label-flipping. Our results confirm that combining clustering and a reputation system succeeds in detecting a wide range of Byzantine behaviors, including colluding attackers, which highlights RADAR's versatility.

802.11 Fine Timing Measurement is an indoor ranging technique. Because it is unauthenticated and unprotected, our experiments indicate that an adversary can implement ranging and location attacks, causing an unsuspecting client to incorporate forged values into its location computation. FTM clients tend to range against a small set of responders (top 3 to 6 responders with strongest signal). Once ranges have been collected, the client can compute its location using various techniques, such as 3-sphere intersection, matrix error minimization techniques or Kalman filter. Irrespective of the technique, we show in this paper that an attacker can cause a ranging client to deviate from its intended path, which can have dire consequences in some settings (e.g., automatic shuttle in public venue causing damages). We also show that protection intended for attacks on comparable ranging techniques, like GPS, are ineffective in the case of FTM.

Bitcoin system (or Bitcoin) is a peer-to-peer and decentralized payment system that uses cryptocurrency named bitcoins (BTCs) and was released as open-source software in 2009. Unlike fiat currencies, there is no centralized authority or any statutory recognition, backing, or regulation for Bitcoin . All transactions are confirmed for validity by a network of volunteer nodes (miners) and after collective agreement is subsequently recorded into a distributed ledger “Blockchain”. Bitcoin platform has attracted both social and anti-social elements. On the one hand, it is social as it ensures the exchange of value, maintaining trust in a cooperative, community-driven manner without the need for a trusted third party. At the same time, it is anti-social as it creates hurdles for law enforcement to trace suspicious transactions due to anonymity and privacy. To understand how the social and anti-social tendencies in the user base of Bitcoin affect its evolution, there is a need to analyze the Bitcoin system as a network. The current paper aims to explore the local topology and geometry of the Bitcoin network during its first decade of existence. Bitcoin transaction data from 03 Jan 2009 12:45:05 GMT to 08 May 2020 13:21:33 GMT was processed for this purpose to build a Bitcoin user graph. The characteristics, local and global network properties of the user's graph were analyzed at ten intervals between 2009 and 2020 with a gap of one year. Small diameter, skewed distribution of transactions, power-law distributed in and out degrees, disconnected graph, and presence of large connected components were the observations from network analysis . Thus, it could be inferred that despite anti-social tendencies, Bitcoin network shared similarities with other complex networks. Network analysis also uncovered twenty types of legal and anti-social entities operating on Bitcoin and provided a path for uncovering these anti-social entities.

Multidimensional Scaling is commonly used to solve multi-sensor location problems. In this paper, we show that such technique provides poor results in the case of indoor location problems based on 802.11 Fine Timing Measurements, especially when the number of anchors is small. We then propose an iterative approach based on geometric resolution of angle inaccuracies. We show that this geometric approach provides better location accuracy results than other Euclidean Distance Matrix techniques based on Least Square Error logic. We also show that the proposed technique, with the input of one or more known points, can allow a set of fixed sensors to auto-determine their position on a floor plan.

This paper is the first to specify blockchains as a composition of abstract data types all together with a hierarchy of consistency criteria that formally characterizes the histories admissible for distributed programs that use them. The paper presents as well some results on implementability of the presented abstractions and a mapping of representative existing blockchains from both academia and industry in our framework.

The presented work continues the line of recent distributed computing community efforts dedicated to the theoretical aspects of blockchains. This paper is the first to specify blockchains as a composition of abstract data types all together with a hierarchy of consistency criteria that formally characterizes the histories admissible for distributed programs that use them. Our work is based on an original oracle-based construction that, along with new consistency definitions, captures the eventual convergence process in blockchain systems. The paper presents as well some results on implementability of the presented abstractions and a mapping of representative existing blockchains from both academia and industry in our framework.

We propose a new way to organise both transactions and blocks in a distributed ledger to address the performance issues of permissionless ledgers. In contrast to most of the existing solutions in which the ledger is a chain of blocks extracted from a tree or a graph of chains, we present a distributed ledger whose structure is a balanced directed acyclic graph of blocks. We call this specific graph a SYC-DAG. We show that a SYC-DAG allows us to keep all the remarkable properties of the Bitcoin blockchain in terms of security, immutability, and transparency, while enjoying higher throughput and self-adaptivity to transactions demand. To the best of our knowledge, such a design has never been proposed so far.

Decentralized cryptocurrency systems offer a medium of exchange secured by cryptography, without the need of a centralized banking authority. Among others, Bitcoin is considered as the most mature one. Its popularity lies on the introduction of the concept of the blockchain, a public distributed ledger shared by all participants of the system. Double spending attacks and blockchain forks are two main issues in blockchain-based protocols. The first one refers to the ability of an adversary to use the very same bitcoin more than once, while blockchain forks cause transient inconsistencies in the blockchain. We show through probabilistic analysis that the reliability of recent solutions that exclusively rely on a particular type of Bitcoin actors, called miners, to guarantee the consistency of Bitcoin operations, drastically decreases with the size of the blockchain.

The context of this work is the online characterization of errors in large scale systems. In particular, we address the following question: Given two successive configurations of the system, can we distinguish massive errors from isolated ones, the former ones impacting a large number of nodes while the second ones affect solely a small number of them, or even a single one? The rationale of this question is twofold. First, from a theoretical point of view, we characterize errors with respect to their neighbourhood, and we show that there are error scenarios for which isolated and massive errors are indistinguishable from an omniscient observer point of view. We then relax the definition of this problem by introducing unresolved configurations, and exhibit necessary and sufficient conditions that allow any node to determine the type of errors it has been impacted by. These conditions only depend on the close neighbourhood of each node and thus are locally computable. We present algorithms that implement these conditions, and show through extensive simulations, their performances. Now from a practical point of view, distinguishing isolated errors from massive ones is of utmost importance for networks providers. For instance, for Internet service providers that operate millions of home gateways, it would be very interesting to have procedures that allow gateways to self distinguish whether their dysfunction is caused by network-level errors or by their own hardware or software, and to notify the service provider only in the latter case.

RRABIDS (Ruby on Rails Anomaly Based Intrusion Detection System) is an application level intrusion detection system for applications implemented with the Ruby on Rails framework. It is aimed at detecting attacks against data in the context of web applications. This anomaly based IDS focuses on the modeling of the application profile in the absence of attacks (called normal profile) using invariants. These invariants are discovered during a learning phase. Then, they are used to instrument the web application at source code level, so that a deviation from the normal profile can be detected at run-time. This paper illustrates on simple examples how the approach detects well known categories of web attacks that involve a state violation of the application, such as SQL injections. Finally, an assessment phase is performed to evaluate the accuracy of the detection provided by the proposed approach.

In this paper we consider the problem of targeted attacks in large scale peer-to-peer overlays. These attacks aimed at exhausting key resources of targeted hosts to diminish their capacity to provide or receive services. To defend the system against such attacks, we rely on clustering and implement induced churn to preserve randomness of nodes identifiers so that adversarial predictions are impossible. We propose robust join, leave, merge and split operations to discourage brute force denial of services and pollution attacks. We show that combining a small amount of randomization in the operations, and adequately tuning the sojourn time of peers in the same region of the overlay allows first to decrease the effect of targeted attacks at cluster level, and second to prevent pollution propagation in the whole overlay.

In this paper we present an analytic study of the impact of churn in cluster-based overlay networks. Cluster-based overlays keep the best of unstructured and structured overlays in terms of scalability, fault-tolerance and stability. Most of join and leave events have no impact on the overall overlay topology making these overlays highly robust to high churn. The only situations that effectively give rise to topology modifications are when clusters need to split because they exceed some maximal size or need to merge because they fall under some minimal size. Although these operations are scalable, they are intricate in the sense that they need synchronization among nodes involved in these operations. In this paper we accurately predict the frequency at which the topology of the overlay changes according to the number of join/leave operations. Our analysis improves upon existing studies by showing that these relevant topological changes are very infrequent, namely θ(N) join/leave events are required before any of these topological operations occur, where N is the number of peers currently in the system. Such a result clearly demonstrates the appropriateness of these overlays to high churn.

Awerbuch and Scheideler have shown that peer-to-peer overlays networks can survive Byzantine attacks only if malicious nodes are not able to predict what will be the topology of the network for a given sequence of join and leave operations. In this paper we investigate adversarial strategies by following specific protocols. Our analysis demonstrates first that an adversary can very quickly subvert DHT-based overlays by simply never triggering leave operations. We then show that when all nodes (honest and malicious ones) are imposed on a limited lifetime, the system eventually reaches a stationary regime where the ratio of polluted clusters is bounded, independently from the initial amount of corruption in the system.